3 Types of Employees That Can Use AI Offerings on Google Cloud

The Google Cloud Platform (GCP) comes with a number of services, databases, and tools to operationalize company-wide data management and analytics. With the insights and accessibility provided, you can leverage data into artificial intelligence (AI) and machine learning (ML) projects cost-efficiently. GCP empowers employees to apply their ideas and experience into data-based solutions and innovation for business growth. Here’s how.

1. Developers without Data Science Experience

With GCP, developers can connect their software engineering experience with AI capabilities to produce powerful results. Using product APIs, developers can incorporate ML into the product without ever having to build a model.

Let’s take training videos for example – Your company has thousands of training videos varying in length and across subjects. They include everything from full-day trainings on BigQuery, to minutes-long security trainings. How do you operationalize all that information for employees to quickly find exactly what they want?

Using Google’s Cloud Video Intelligence API, the developer can transcribe not only every single video, word-for-word, but also document the start and end time of every word, in every video. The developer builds a search index on top of the API, and just like that, users can search specific content in thousands of videos. Results display both the relevant videos and timestamps within the videos, where the keyword is found. Now employees can immediately find the topic they want to learn more about, without needing to sift through what could be hours of unrelated information.

Additional APIs include, Cloud Natural Language, Speech-to-Text, Text-to-Speech, Cloud Data Loss Prevention, and many others in ML.

2. Everyone without Data Science Experience, who isn’t a Developer

Cloud AutoML enables your less technical employees to harness the power of machine learning. It bridges the gap between the API and building your own ML model. Using AutoML, anyone can create custom models tailored to your business needs, and then integrate those models into applications and websites.

For this example, let’s say you’re a global organization who needs to translate communications across dialects and business domains. The intricacies and complexities of natural language require expensive linguists and specialist translators with domain-specific expertise. How do you communicate in real time effectively, respectfully, and cost-efficiently?

With AutoML Translation, almost anyone can create translation models that return query results specific to your domain, in 50 different language pairs. It graphically ingests your data from any type of Sheet or CSV file. The input data necessary is pairs of sentences that mean the same thing in both the language you want to translate from, and the one you want to translate to. Google goes the extra mile between generic translation and specific, niche vocabularies with an added layer of specificity to help the model get the right translation for domain-specific material. Within an hour, the model translates based on your domain, taxonomy, and the data you provided.

Cloud AutoML is available for platform, sight, structured data, and additional language capabilities.

3. Data Scientists

Data scientists have the experience and data knowledge to take full advantage of GCP AI tools for ML. One of the issues data scientists often confront is notebook functionality and accessibility. Whether its TensorFlow, PyTorch, or JupyterLab, these open source ML platforms require too many resources to run on a local computer, or easily connect to BigQuery.

Google AI Platform Notebooks is a managed service that provides a pre-configured environment to support these popular data science libraries. From a security standpoint, AI Platform Notebooks is attractive to enterprises for the added security of the cloud. Relying on a local device, you run the risk of human error, theft, and fatal accidents. Equipped with a hosted, integrated, secure, and protected JupyterLab environment, data scientists can do the following:

  • Virtualize in the cloud
  • Connect to GCP tools and services, including BigQuery
  • Develop new models
  • Access existing models
  • Customize instances
  • Use Git / GitHub
  • Add CPUs, RAM, and GPUs to scale
  • Deploy models into production
  • Backup machines

With a seamless experience from data to a deployed ML model, data scientists are empowered to work faster, smarter, and safer. Contact Us to further your organization’s ability to maximize data, AI, and ML.

Here are a few resources for those who wish to learn more about this subject:

Sam Tawfik, Sr Product Marketing Manager

Maximizing Cloud Data with Google Cloud Platform Services

If you’re trying to run your business smarter, not harder, utilizing data to gain insights into decision making gives you a competitive advantage. Cloud data offerings empower utilization of data in the cloud, and the Google Cloud Platform (GCP) is full of options. Whether you’re migrating data, upgrading to enterprise-class databases, or transforming customer experience on cloud-native databases – Google Cloud services can fit your needs.

Highlighting some of what Google has to offer

With so many data offerings from GCP, it’s nearly impossible to summarize them all. Some are open source projects being distributed by other vendors, while others were organically created by Google to service their own needs before being externalized to customers. A few of the most popular and widely used include the following.

  • BigQuery: Core to GCP, this serverless, scalable, multi-cloud, data warehouse enables business agility – including data manipulation and data transformation, and it is the engine for AI, machine learning (ML), and forecasting.
  • Cloud SQL: Traditional relational database in the cloud that reduces maintenance costs with fully managed services for MySQL, PostgreSQL, and SQL Server.
  • Spanner: Another fully managed relational database offering unlimited scale, consistency, and almost 100% availability – ideal for supply chain and inventory management across regions and between two databases.
  • Bigtable: Low latency, NoSQL, fully managed database for ML and forecasting, using very large amounts of data in analytical and operational workloads.
  • Data Fusion: Fully managed, cloud-native data integration tool that enables you to move different data sources to different targets – includes over 150 preconfigured connectors and transformers.
  • Firestore: From the Firebase world comes the next generation of Datastore. This cloud-native, NoSQL, document database lets you develop custom apps that directly connect to the database in real-time.
  • Cloud Storage: Object based storage can be considered a database because of all the things you can do with BigQuery – including using standard SQL language to query objects in storage.

Why BigQuery?

After more than 10 years of development, BigQuery has become a foundational data management tool for thousands of businesses. With a large ecosystem of integration partners and a powerful engine that shards queries across petabytes of data and delivers a response in seconds, there are many reasons BigQuery has stood the test of time. It’s more than just super speed, data availability, and insights.

Standard SQL language
If you know SQL, you know BigQuery. As a fully managed platform, it’s easy to learn and use. Simply populate the data and that’s it! You can also bring in large public datasets to experiment and further learn within the platform.

Front-end data
If you don’t have Looker, Tableau, or another type of business intelligence (BI) tool to visualize dashboards off of BigQuery, you can use the software development kit (SDK) for web-based front-end data display. For example, government health agencies can show the public real-time COVID-19 case numbers as they’re being reported. The ecosystem of BigQuery is so broad that it’s a source of truth for your reports, dashboards, and external data representations.

Analogous across offerings

Coming from on-prem, you may be pulling data into multiple platforms – BigQuery being one of them. GCP offerings have a similar interface and easy navigation, so functionality, user experience, and even endpoint verbs are the same. Easily manage different types of data based on the platforms and tools that deliver the most value.

BigQuery Omni

One of the latest GCP services was built with a similar API and platform console to various other platforms. The compatibility enables you to query data living in other places using standard SQL. With BigQuery Omni, you can connect and combine data from outside GCP without having to learn a new language.

Ready for the next step in your cloud journey?

As a Google Cloud Partner, 2nd Watch is here to be your trusted cloud advisor throughout your cloud data journey, empowering you to fuel business growth while reducing cloud complexity. Whether you’re embracing cloud data for the first time or finding new opportunities and solutions with AI, ML, and data science our team of data scientists can help. Contact Us for a targeted consultation and explore our full suite of advanced capabilities.

Learn more

Webinar: 6 Essential Tactics for your Data & Analytics Strategy

Webinar:  Building an ML foundation for Google BigQuery ML & Looker

-Sam Tawfik, Sr Product Marketing Manager

3 Ways McDonald’s France is Preparing their Data for the Future

Data access is one of the biggest influences on business intelligence, innovation, and strategy to come out of digital modernization. Now that so much data is available, the competitive edge for any business is derived from understanding and applying it meaningfully. McDonald’s France is gaining business-changing insights after migrating to a data lake, but it’s not just fast food that can benefit. Regardless of your industry, gaining visibility into and governance around your data is the first step for what’s next.

1. No More Manual Legacy Tools

Businesses continuing to rely on spreadsheets and legacy tools that require manual processes are putting in a lot more than they’re getting out. Not only are these outdated methods long, tedious, subject to human error, and expensive in both time and resources – but there’s a high probability the information is incomplete or inaccurate. Data-based decision making is powerful, however, without a data platform, a strong strategy, automation, and governance, you can’t easily or confidently implement takeaways.

Business analysts at McDonald’s France historically relied on Excel-based modeling to understand their data. Since partnering with 2nd Watch, they’ve been able to take advantage of big data analytics by leveraging a data lake and data platform. Architected from data strategy and ingestion, to management and pipeline integration, the platform provides business intelligence, data science, and self-service analytics. Now, McDonald’s France can rely on their data with certainty.

2. Granular Insights Become Opportunities for Smart Optimization

Once intuitive solutions for understanding your data are implemented, you gain finite visibility into your business. Since completing the transition from data warehouse to data lake, McDonald’s France has new means to integrate and analyze data at the transaction level. Aggregate information from locations worldwide provides McDonald’s with actionable takeaways.

For instance, after establishing the McDonald’s France data lake, one of the organization’s initial projects focused on speed of service and order fulfilment. Speed of service encompasses both food preparation time and time spent talking to customers in restaurants, drive-thrus, and on the online application. Order fulfilment is the time it takes to serve a customer – from when the order is placed to when it’s delivered. With transaction-level purchase data available, business analysts can deliver specific insights into each contributing factor of both processes. Maybe prep time is taking too long because restaurants need updated equipment, or the online app is confusing and user experience needs improvement. Perhaps the menu isn’t displayed intuitively and it’s adding unnecessary time to speed of service.

Multiple optimization points provide more opportunity to test improvements, scale successes, apply widespread change, fail fast, and move ahead quickly and cost-effectively. Organizations that make use of data modernization can evolve with agility to changing customer behaviors, preferences, and trends. Understanding these elements empowers businesses to deliver a positive overall experience throughout their customer journey – thereby impacting brand loyalty and overall profit potential.

3. Machine Learning, Artificial Intelligence, and Data Science

Clean data is absolutely essential for utilizing machine learning (ML), artificial intelligence (AI), and data science to conserve resources, lower costs, enable customers and users, and increase profits. Leveraging data for computers to make human-like decisions is no longer a thing of the future, but of the present. In fact, 78% of companies have already deployed ML, and 90% of them have made more money as a result.

McDonald’s France identifies opportunity as the most important outcome of migrating to a data lake and strategizing on a data platform. Now that a wealth of data is not only accessible, but organized and informative, McDonald’s looks forward to ML implementation in the foreseeable future. Unobstructed data visibility allows organizations in any industry to predict the next best product, execute on new best practices ahead of the competition, tailor customer experience, speed up services and returns, and on, and on. We may not know the boundaries of AI, but the possibilities are growing exponentially.

Now it’s Time to Start Preparing Your Data

Organizations worldwide are revolutionizing their customer experience based on data they already collect. Now is the time to look at your data and use it to reach new goals. 2nd Watch Data and Analytics Services uses a five-step process to build a modern data management platform with strategy to ingest all your business data and manage the data in the best fit database. Contact Us to take the next step in preparing your data for the future.

-Ian Willoughby, Chief Architect and Vice President

Listen to the McDonald’s team talk about this project on the 2nd Watch Cloud Crunch podcast.

Cloud Crunch Podcast: Data, AI & ML on Google Cloud

If you’re trying to run your business smarter, not harder, chances are you’re utilizing data to gain insights into the decision-making process and gain a competitive advantage. In the latest episode of our podcast, we talk with data and AI & ML expert, Rui Costa at Google Cloud, about why and when to use cloud data offerings and how to make the most of your data in the cloud. Listen now on Spotify, iTunes, iHeart Radio, Stitcher, or wherever you get your podcasts.

We’d love to hear from you! Email us at CloudCrunch@2ndwatch.com with comments, questions and ideas.

Top Enterprise IT Trends for 2021

Between the global pandemic and the resulting economic upheaval, it’s fair to say many businesses spent 2020 in survival mode. Now, as we turn the page to 2021, we wonder what life will look like in this new normalcy. Whether it is employees working from home, the shift from brick and mortar to online sales and delivery, or the need to accelerate digital transformation efforts to remain competitive, 2021 will be a year of re-invention for most companies.

How might the new normal impact your company? Here are five of the top technology trends we predict will drive change in 2021:

1. The pace of cloud migration will accelerate

Most companies, by now, have started the journey to the public cloud or to a hybrid cloud environment. The events of 2020 have added fuel to the fire, creating an urgency to maximize cloud usage within companies that now understand that the speed, resilience, security and universal access provided by cloud services is vital to the success of the organization.

“By the end of 2021, based on lessons learned in the pandemic, most enterprises will put a mechanism in place to accelerate their shift to cloud-centric digital infrastructure and application services twice as fast as before the pandemic,” says Rick Villars, group vice president, worldwide research at IDC. “Spending on cloud services, the hardware and software underpinning cloud services, and professional and managed services opportunities around cloud services will surpass $1 trillion in 2024,”

The progression for most companies will be to ensure customer-facing applications take priority. In the next phase of cloud migration, back-end functionality embodied in ERP-type applications will move to the cloud. The easiest and fastest way to move applications to the cloud is the simple lift-and-shift, where applications remain essentially unchanged. Companies looking to improve and optimize business processes, though, will most likely refactor, containerize, or completely re-write applications. They will turn to “cloud native” approaches to their applications.

2. Artificial intelligence (AI) and machine learning (ML) will deliver business insight

Faced with the need to boost revenue, cut waste, and squeeze out more profits during a period of economic and competitive upheaval, companies will continue turning to AI and machine learning to extract business insight from the vast trove of data most collect routinely, but don’t always take advantage of.

According to a recent PwC survey of more than 1,000 executives, 25% of companies reported widespread adoption of AI in 2020, up from 18% in 2019. Another 54% are moving quickly toward AI. Either they have started implementing limited use cases or they are in the proof-of-concept phase and are looking to scale up. Companies report the deployment of AI is proving to be an effective response to the challenges posed by the pandemic.

Ramping up AI and ML capabilities in-house can be a daunting task, but the major hyperscale cloud providers have platforms that enable companies to perform AI and ML in the cloud. Examples include Amazon’s SageMaker, Microsoft’s Azure AI and Google’s Cloud AI.

Edge computing will take on greater importance

For companies that can’t move to the cloud because of regulatory or data security concerns, edge computing is emerging as an attractive option. With edge computing, data processing is performed where the data is generated, which reduces latency and provides actionable intelligence in real time. Common use cases include manufacturing facilities, utilities, transportation, oil and gas, healthcare, retail and hospitality.

The global edge computing market is expected to reach $43.4 billion by 2027, fueled by an annual growth rate of nearly 40%, according to a report from Grand View Research.

The underpinning of edge computing is IoT, the instrumentation of devices (everything from autonomous vehicles to machines on the factory floor to a coffee machine in a fast-food restaurant) and the connectivity between the IoT sensor and the analytics platform. IoT platforms generate a vast amount of real-time data, which must be processed at the edge because it would too expensive and impractical to transmit that data to the cloud.

Cloud services providers recognize this reality and are now bringing forth specific managed service offerings for edge computing scenarios, such as Amazon’s new IoT Greengrass service that extends cloud capabilities to local devices, or Microsoft’s Azure IoT Edge.

4. Platform-as-a-Service will take on added urgency

To increase the speed of business, companies are shifting to cloud platforms for application development, rather than developing apps in-house. PaaS offers a variety of benefits, including the ability to take advantage of serverless computing delivering scalability, flexibility and quicker time to develop and release new apps. Popular serverless platforms include Amazon Lambda and Microsoft’s Azure Functions.

5. IT Automation will increase

Automating processes across the entire organization is a key trend for 2021, with companies prioritizing and allocating money for this effort. Automation can cut costs and increase efficiency in a variety of areas – everything from Robotics Process Automation (RPA) to automate low-level business processes, to the automation of security procedures such as anomaly detection or incident response, to automating software development functions with new DevOps tools.

Gartner predicts that, through 2024, enhancements in analytics and automatic remediation capabilities will refocus 30% of IT operations efforts from support to continuous engineering. And by 2023, 40% of product and platform teams will use AIOps for automated change risk analysis in DevOps pipelines, reducing unplanned downtime by 20%.

Tying it all together

These trends are not occurring in isolation.  They’re all part of the larger digital transformation effort that is occurring as companies pursue a multi-cloud strategy encompassing public cloud, private cloud and edge environments. Regardless of where the applications live or where the processing takes place, organizations are seeking ways to use AI and machine learning to optimize processes, conduct predictive maintenance and gain critical business insight as they try to rebound from the events of 2020 and re-invent themselves for 2021 and beyond.

Where will 2021 take you? Contact us for guidance on how you can take hold of these technology trends to maximize your business results and reach new goals.

-Mir Ali, Field CTO

3 Steps to Implementing a Machine Learning Project

Once you understand the benefits and structure of data science and machine learning (ML), it’s time to start implementation. While it’s not an overly complicated process, planning change management from implementation through replication can help mitigate potential pitfalls. We recommend following this 3-step process.

Step 1: Find Your Purpose

It can be fun to tinker around with shiny, new technology toys, but without specific goals, the organization suffers. Time and resources are wasted, and without proof of value-added, the buy-in necessary from leadership won’t happen. Why are you implementing this solution, and what do you hope to get out of the data you put in?

ML projects can produce several outcomes contributing to decisions fueled by data and gaining insights into customer buying behavior, which can be used to optimize the sales cycle with new marketing campaigns. Other uses could include utilizing predictive search to improve user experience, streamlining warehouse inventory with image processing, real-time fraud detection, predictive maintenance, or elevating customer service with voice to text speech recognition.

ML projects are typically led by a data scientist who is responsible for understanding the business requirements and who leverages data to train a computer model to learn patterns in very large volumes of data to predict outcomes while also improving the outcomes over time.

Successful ML solutions can generate 4-5% higher profit margins, so identify benchmarks, set growth goals, and integrate regular progress measurements to make sure you’re always on track with your purpose in mind.

Step 2: Apply Machine Learning

The revolutionary appeal for ML is that it does not require an explicit computer program to deliver analytics and predictions, it leverages a computer model that can be trained to predict and improve the outcomes. After the data scientist’s analysis defines the business requirements, they wrangle the necessary data to train the ML model by leveraging an algorithm, which is the engine that turns the data into a model.

Data Wrangling

Data preparation is critical to the success of the ML project because it is the foundation of everything that follows. Garbage in equals garbage out, but value in produces more value.

Raw data can be tempting, but data that isn’t clean, governed, and appropriate for business use corrupts the model and invalidates the outcome. Data needs to be prepared and ready, meaning it has been reviewed for accuracy, and it’s available and accessible to all users. Data is typically stored in a cloud data warehouse or data lake and it must be maintained with ongoing governance.

A common mistake organizations make is relying on data scientists to clean the data. Studies have found that data scientists spend 70% of their time wrangling data and only 30% of the time implementing the solution and delivering business value. These highly paid and skilled professionals are scarce resources trained for innovation and analyzing data, not cleaning data. Only after the data is clean should data scientists start their analysis.

ML Models

The data scientist’s core expertise is in selecting the appropriate algorithm to process and analyze the data. The science in ML is figuring out which algorithm to use and how to optimize it to deliver accurate and reliable results.

Thankfully, ML algorithms are available today in all the major service provider platforms, and many Python and R libraries. The general use cases within reach include:

  • Classification (is this a cat or is this not a cat) using anomaly detection, marketing segmentation, and recommendation engines.
  • NLP (natural language progression) using autocomplete, sentiment, and understanding (i.e., chatbots).
  • Timeseries using forecasting.

Algorithms are either supervised or unsupervised. Supervised learning algorithms start with training data and correct answers. Labeled data trains the model using the algorithm and feedback. Think texting and autocorrect – the algorithm is always learning new words based on your interaction with autocorrect. That feedback is delivered to the live model for updates and the feedback loop never ends.

Unsupervised learning algorithms start with unlabeled data. The algorithm divides the data into meaningful clusters used to make inferences about the records. These algorithms are useful for segmentation of click stream data or email lists.

Some popular algorithms include CNN (convolutional neuro network), a deep learning algorithm, K Means Clustering, PCA, Support Vector Machine, Decision Trees, and Logistic Regression.

Model Quality

With everything in place, it’s time to see if the model is doing what you need it to do. When evaluating model quality, consider bias and variance. Bias quantifies the algorithm’s limited flexibility to learn the pattern. Variance quantifies the algorithm’s sensitivity to specific sets of training.

Three things can happen when optimizing the model:

  1. Over-fitting: Low bias + high variance. The model is too tightly fitted to the training data, and it won’t generalize data it hasn’t seen before.
  2. Under-fitting: High bias + low variance. The model is new and hasn’t reached a point of accuracy. Get to over-fitting first, then back up and reiterate until the model fits.
  3. Limiting/preventing under/over-fitting: There are too many features in the model (i.e. data points used to build the model), and you need to either reduce them, or create new features from existing features.

Before unleashing your ML project on customers, experiment first with employees. Solutions like virtual assistance and chat bots that are customer-facing can jeopardize your reputation if they don’t add value to interactions with customers. Because ML influences decision-making, accuracy is a must before real-world implementation.

Step 3: Experiment and Push into Production

With software projects, it either works or it crashes. With data science projects, you have to see, touch, and feel the results to know if it’s working. Reach out to users for feedback and to ensure any changes to user experience are positive. Luckily, with the cloud, the cost of experimentation is low, so don’t be afraid to beta test before a full launch.

Once the model fits and you’ve pushed the project into production, make noise about it around the organization. Promote that you’re implementing something new and garner the attention of executive leadership. Unfortunately, 70% of data projects fail because they don’t have an executive champion.

Share your learnings internally using data, charts, results, and emphasizing company-wide impact. You’re not going to get buy in on day one, but as you move up the chain of command, earning more and more supporters, your budget will allow for more machine learning solutions. Utilize buzzwords and visual representations of the project – remember data science needs to be seen, touched, and felt.

Ensure ML and data science success with best practices for introducing, completing, and repeating implementation. 2nd Watch Data and Analytic Solutions help your organization realize the power of ML with proper data cleaning, the right algorithm selection, and quality model deployment. Contact Us to see how you can do more with the data you have.

-Sam Tawfik, Sr Marketing Manager, Data & Analytics

Understanding Data Science, Artificial Intelligence, and Machine Learning

Amazing possibilities are available in data science with artificial intelligence (AI) and machine learning (ML). Large sets of data, inexpensive storage options, and cloud processing capabilities are enabling computers to make human-like decisions. Across industries, businesses are leveraging these algorithm-based models to save time, reduce costs, enable users, and grow profits.

What’s the difference between Data Science, Artificial Intelligence, and Machine Learning

Data science, AI, and ML can get lumped together, but there are some distinctions to understand. Simply put, AI is a computer doing things that typically would require human scrutiny or reasoning. ML is the application of statistical learning techniques to automatically learn patterns in data. These patterns are used to develop a model to make more accurate predictions about the world. And both terms utilize data science to accomplish outcomes.

With these central terms defined, we recommend using ‘machine learning’ or ‘ML’ to describe data science projects internally because there is sometimes an aura of fear around AI that “the robots are going to take my job.” Although joking (a bit), buy-in from executives is critical to a successful data project, so ML is recommended over AI.

Utilizing Machine Learning for Profit Growth

A recent study showed 78% of companies have already deployed ML, and 90% of them have made more money as a result. Manufacturing and supply-chain management are experiencing the largest average cost decrease, and marketing, sales, product and service development are reaching the highest average revenue gains. Additionally, a McKinsey survey revealed that organizations with a high diffusion of ML had 4-5% higher profit margins than their peers with no ML. Not only can ML reduce your overall costs, but it also enables you to grow your bottom line. If your organization is not utilizing ML, now is the time to start.

From Data to Model

Machine learning is already a staple in many of the functions we utilize daily. Predictive search in Google and within catalogues, fraud detection on suspicious credit card purchases, near-instant credit approval, social network suggestions via mutual connections, and voice recognition are all common today. Behind these intelligent decisions is a model that acts as a function or program. The model is trained on sample data using a machine learning algorithm to learn patterns. Based on the information learned about the sample data, the model is applied to inputs it may or may not have seen before and predicts an outcome.

Traditional programming depends on the written program and the input data it’s fed. The computer runs the program against the data, and you get an output directly tied to the logic or function of the program. Only the data that can be processed by the program gets analyzed, and outliers are removed.

In ML, the computer is still given input data. For example, what you know about your customer – time stamps, demographics, spend, etc. – but it doesn’t have a written program. Instead, it’s given the output you desire. For example, you might want to know which customers churn. Then you build a model by training programmed algorithms to analyze input data and predict an output. Essentially, the model recognizes the correlation between the output results and the input data. Here, the model utilizes algorithms to identify patterns in data that that heavily influence the customer churn score.

In this example, an organization might discover that most customers stop doing business with them after a certain promo ends, or a high percentage of customers who come in through a specific lead gen pipeline don’t stay for long. Using this information, the organization can make informed and specific decisions about how to reduce churn based on known patterns.

All relevant data is taken into account in ML to deliver a more comprehensive story about why things are happening in your organization. Machine learning can quickly affirm or discredit intuition and allow organizations to fail faster, and in the right direction, to meet overall goals more efficiently.

A best practices approach is necessary to streamline the process of introducing, completing, and repeating a data science project. With 2nd Watch Data and Analytics Services, you realize the power of machine learning with the right algorithm selection and model deployment. Contact Us to see how machine learning can positively impact your organization or download our eBook, “Artificial Intelligence and Machine Learning: 3 Steps to Set the Table for Data Science in 2021” to learn about the 3 steps necessary to producing valid and applicable results from your data science project.

– Rob Whelan, Practice Director, Data & Analytics

7 Trends Influencing DevSecOps & DevOps Adoption

Companies worldwide have been increasing DevOps adoption and DevSecOps adoption into their regular workflows at an exponential rate. Whether following Agile methodologies or creating independent workflows stemming from DevOps, companies have been leveraging the faster manufacturing rate with superior quality that DevSecOps provides.

However, the increasing development in autonomous technologies such as AI or ML is idealizing a work cycle where the system operates independently of humans. It aims to provide faster, reliable, and better products – shifting from DevOps to NoOps.

A set of practices coupling software development (Dev) and information technology operations (Ops), DevOps is the combination of employees, methods, and products to allow for perpetual, seamless delivery of quality and value. Adding security to a set of DevOps practices, a DevSecOps approach provides multiple layers of security and reliability by integrating highly secure, robust, and dependable processes and tools into the work cycle and the final product.

This desirable outcome of integrating DevOps and DevSecOps into corporations has made it a trendy work cycle in the market. However, with a growing focus on automation and development in Artificial Intelligence and Machine Learning, we could be heading into a NoOps scenario, where self-learning and self-healing systems govern the work processes.

NoOps is a work cycle wherein the technologies used by a company are so autonomous and intelligent that DevOps and DevSecOps do not need to be exclusively implemented to maintain a continuous outflow of quality and value.

What are the trends that truly influence DevOps and DevSecOps adoptions in countless tech businesses – small and large – all across the globe? Download our 7 Trends Influencing DevOps/DevSecOps Adoption to find out.

-Mir Ali, Field CTO

Cloud Autonomics and Automated Management and Optimization: Update

The holy grail of IT Operations is to achieve a state where all mundane, repeatable remediations occur without intervention, with a human only being woken for any action that simply cannot be automated.  This allows not only for many restful nights, but it also allows IT operations teams to become more agile while maintaining a proactive and highly-optimized enterprise cloud.  Getting to that state seems like it can only be found in the greatest online fantasy game, but the growing popularity of “AIOps” gives great hope that this may actually be closer to a reality than once thought.

Skeptics will tell you that automation, autonomics, orchestration, and optimization have been alive and well in the datacenter for more than a decade now. Companies like Microsoft with System Center, IBM with Tivoli, and ServiceNow are just a few examples of autonomic platforms that harness the ability to collect, analyze and make decisions on how to act against sensor data derived from physical/virtual infrastructure and appliances.  But when you couple these capabilities with advancements brought through AIOps, you are able take advantage of the previously missing components by incorporating big data analytics along with artificial intelligence (AI) and Machine Learning (ML).

As you can imagine, these advancements have brought an explosion of new tooling and services from Cloud ISV’s thought to make the once utopian Autonomic cloud a reality. Palo Alto Network’s Prisma Public Cloud product is great example of a technology that functions with autonomic capabilities.  The security and compliance features of Prisma Public Cloud are pretty impressive, but it also has a component known as User and Entity Behavior Analytics (UEBA).  UEBA analyzes user activity data from logs, network traffic and endpoints and correlates this data with security threat intelligence to identify activities—or behaviors—likely to indicate a malicious presence in your environment. After analyzing the current state of the vulnerability and risk landscape, it reports current risk and vulnerability state and derives a set of guided remediations that can be either performed manually against the infrastructure in question or automated for remediation to ensure a proactive response, hands off, to ensure vulnerabilities and security compliance can always be maintained.

Another ISV focused on AIOps is MoogSoft who is bringing a next generation platform for IT incident management to life for the cloud.  Moogsoft has purpose-built machine learning algorithms that are deigned to better correlate alerts and reduce much of the noise associated with all the data points. When you marry this with their Artificial Intelligence capabilities for IT operations, they are helping DevOps teams operate smarter, faster and more effectively in terms of automating traditional IT operations tasks.

As we move forward, expect to see more and more AI and ML-based functionality move into the core cloud management platforms as well. Amazon recently released AWS Control Tower to aide your company’s journey towards AIOps.  While coming with some pretty incredible features for new account creation and increased multi-account visibility, it uses service control policies (SCPs) based upon established guardrails (rules and policies).  As new resources and accounts come online, Control Tower can force compliance with the policies automatically, preventing “bad behavior” by users and eliminating the need to have IT configure resources after they come online. Once AWS Control Tower is being utilized, these guardrails can apply to multi-account environments and new accounts as they are created.

It is an exciting time for autonomic platforms and autonomic systems capabilities in the cloud, and we are excited to help customers realize the many potential capabilities and benefits which can help automate, orchestrate and proactively maintain and optimize your core cloud infrastructure.

To learn more about autonomic systems and capabilities, check out Gartner’s AIOps research and reach out to 2nd Watch. We would love to help you realize the potential of autonomic platforms and autonomic technologies in your cloud environment today!

-Dusty Simoni, Sr Product Manager




AWS re:Invent Keynote Recap – Wednesday

I have been looking forward to Andy Jassy’s keynote since I arrived in Las Vegas. Like the rest of the nearly 50k cloud-geeks in attendance, I couldn’t wait to learn about all of the cool new services and feature enhancements that will be unleashed that can solve problems for our clients, or inspire us to challenge convention in new ways.

Ok, I’ll admit it. I also look forward to the drama of the now obligatory jabs at Oracle, too!

Andy’s 2017 keynote was no exception to the legacy of previous re:Invents on those counts, but my takeaway from this year is that AWS has been able to parlay their flywheel momentum of growth in IaaS to build a wide range of higher-level managed services. The thrill I once got from new EC2 instance type releases has given way to my excitement for Lambda and event-based computing, edge computing and IoT, and of course AI/ML!


Of all the topics covered in the keynote, the theme that continues to resonate throughout this conference for me is that AWS wants people to know that they are the leader in AI and machine learning. As an attendee, I received an online survey from Amazon prior to the conference asking for my opinion on AWS’s position as a leader in the AI/ML space. While I have no doubts that Amazon has unmatched compute and storage capacity, and certainly has access to a wealth of information to train models, how does one actually measure a cloud provider’s AI/ML competency? Am I even qualified to answer without an advanced math degree?

That survey sure makes a lot more sense to me following the keynote as I now have a better idea of what “heavy lifting” a cloud provider can offload from the traditional process.

Amazon has introduced SageMaker, a fully managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models at any scale. It integrates with S3, and with RDS, DynamoDB, and Redshift by way of AWS Glue. It provides managed Jupyter notebooks and even comes supercharged with several common ML algorithms that have been tuned for “10x” performance!

In addition to SageMaker, we were introduced to Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to analyze text. I personally am excited to integrate this into future chatbot projects, but the applications I see for this service are numerous.

After you’ve built and trained your models, you can run them in the cloud, or with the help of AWS Greengrass and its new machine learning inference feature, you can bring those beauties to the edge!

What is a practical application for running ML inference at the edge you might ask?

Dr. Matt Wood demoed a new hardware device called DeepLens for the audience that does just that! DeepLens is a deep-learning enabled wireless video camera specifically designed to help developers of all skill levels grow their machine learning skills through hands-on computer vision tutorials. Not only is this an incredibly cool device to get to hack around with, but it signals Amazon’s dedication to raising the bar when it comes to AI and machine learning by focusing on the wet-ware: hungry minds looking to take their first steps.

Andy’s keynote included much more than just AI/ML, but to me, the latest AI/ML services that were announced on Tuesday represent the signal of Amazon’s future of higher-level services which will keep them the dominant cloud provider into the future.


–Joe Conlin, Solutions Architect, 2nd Watch